In any industrial manufacturing environment, accurate control of the manufacturing process can be essential. Ineffective process control can lead to products that fail to meet desired yield and quality levels. Furthermore, poor process control can significantly increase costs due to increased raw material usage, labor costs and the like. Accordingly, in an effort to gain improved control of the process, many manufacturers seek to develop computational models or simulations for the manufacturing process. A modeling expert, for example, may develop computational models using a variety of tools and a variety of modeling techniques including, for example, neural networks, linear regression, partial least squares (PLS), principal component analysis, and the like.
However, it is often difficult to effectively transfer the process knowledge produced by these models from the expert studying the model to the process engineers and operators on the manufacturing line. Reports produced by such models may be filed and forgotten, information from presentations and training sessions may not be retained, and process models may be left to languish on the computers of the experts who developed them. Furthermore, computational models are often developed using sophisticated modeling and simulation tools that are often unavailable at the manufacturing line, and are often too cumbersome and complex for use by the line operator.